Top-N Recommendations
from Implicit Feedback
leveraging Linked Open Data
Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di ...
Outline
 Introduction and motivation
 SPrank: Semantic Path-based ranking
 Data model and Problem formulation
 Path-ba...
Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion...
Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion...
Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail

subject

predicate

object

8134 triples

RecSys 2013 – 7th ACM Conference...
Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail

Skyscrapers over 350 meters in Hong Kong?
select * where {
?s dbpedia-owl:...
Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail

db:location

db:International_Commerce_centre
db:thumbnail

db:Central_Pla...
Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explic...
Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explic...
Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explic...
Our approach
• Usage of structured semantic data freely available on the Web
(Linked Open Data) to describe items
DBpedia ...
Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based fe...
Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based fe...
Data model
Implicit Feedback Matrix
^

S

I1

i2

i3

i4

u1

1

1

0

0

u2

1

0

1

0

u3

0

1

1

0

u4

0

1

0

Kn...
Data model
Implicit Feedback Matrix
^

S

I1

i2

i3

i4

u1

1

1

0

0

u2

1

0

1

0

u3

0

1

1

0

u4

0

1

0

Kn...
Data model
Implicit Feedback Matrix
^

S

I1

i2

i3

i4

u1

1

1

0

0

u2

1

0

1

0

u3

0

1

1

0

u4

0

1

0

Kn...
Problem formulation

u

^


u

^

I  {i  I | s ui  1}

Set of relevant items for u

I  {i  I | s ui  0}

Set of ir...
Path-based features
path acyclic sequence of relations ( s , .. rl , .. rL )
u3 s i2 p2 e1 p1 i1
xui ( j ) 

 (s, p2 , p...
Path-based features
i1

xu3i1 ?

e1

u1

e3
u2

i2
e2

u3
i3

u4
i4
RecSys 2013 – 7th ACM Conference on Recommender System...
Path-based features
path1 (s, s, s) : 1

i1

e1

u1

e3
u2

i2
e2

u3
i3

u4
i4
RecSys 2013 – 7th ACM Conference on Recomm...
Path-based features
path1 (s, s, s) : 2

i1

e1

u1

e3
u2

i2
e2

u3
i3

u4
i4
RecSys 2013 – 7th ACM Conference on Recomm...
Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 1

i1

e1

u1

e3
u2

i2
e2

u3
i3

u4
i4
RecSys 2013 – 7th AC...
Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2

i1

e1

u1

e3
u2

i2
e2

u3
i3

u4
i4
RecSys 2013 – 7th AC...
Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1

i1

e1

u1

e3
u2

i2
e2

u3
i3

...
Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1

2
xu3i1 (1) 
5
2
xu3i1 (2) 
5
1...
Learning the ranking function
Point-wise Learning To Rank
Learn a prediction function f :

D



^

s.t. f ( xui )  sui

...
BagBoo
BagBoo: a scalable hybrid bagging-the-boosting model
[D. Pavlov, A. Gorodilov, C. Brunk CIKM2010]

• Combination of...
Evaluation Methodology
• Top-N Item recommendation task
• Evaluation methodology similar to:
[Cremonesi, Koren and Turrin,...
Datasets
Subset of Movielens mapped to DBpedia
3,792 users
2,795 movies
104,351 entities

Subset of Last.fm mapped to DBpe...
Evaluation of different ranking functions
Movielens
0,6

0,5

recall@5

0,4

BagBoo

0,3

GBRT
Sum

0,2

0,1

0
given 5

g...
Evaluation of different ranking functions
Last.fm
0,6

0,5

recall@5

0,4

BagBoo

0,3

GBRT
Sum

0,2

0,1

0
given 5

giv...
Comparative approaches
MyMediaLite
• BPRMF, Bayesian Personalized Ranking for Matrix Factorization
• BPRLin, Linear Model ...
Comparison with other approaches
Movielens
0,6

0,5

recall@5

0,4
SPrank
BPRMF

0,3

SLIM
BPRLin

0,2

SMRMF

0,1

0
give...
Comparison with other approaches
Last.fm
0,6

0,5

recall@5

0,4
SPrank
BPRMF

0,3

SLIM
BPRLin

0,2

SMRMF

0,1

0
given ...
Contributions
SPrank: Semantic Path-based ranking
 Combination of semantic item descriptions from the Web
of Data and imp...
Q&A
A Little Semantics Goes a Long Way.
Hendler Hypothesis

RecSys 2013 – 7th ACM Conference on Recommender Systems
Octobe...
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Top-N Recommendations from Implicit Feedback leveraging Linked Open Data

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Transcript of "Top-N Recommendations from Implicit Feedback leveraging Linked Open Data"

  1. 1. Top-N Recommendations from Implicit Feedback leveraging Linked Open Data Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi ostuni@deemail.poliba.it, t.dinoia@poliba.it, disciascio@poliba.it, mirizzi@deemail.poliba.it Polytechnic University of Bari - Bari (ITALY) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  2. 2. Outline  Introduction and motivation  SPrank: Semantic Path-based ranking  Data model and Problem formulation  Path-based features  Learning the ranking function  Experimental Evaluation  Contributions and Conclusion RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  3. 3. Linked Open Data • Initiative for publishing and connecting data on the Web using Semantic Web technologies; • >30 billion of RDF triples from hundreds of data sources; • Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ] RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  4. 4. Linked Open Data • Initiative for publishing and connecting data on the Web using Semantic Web technologies; • >30 billion of RDF triples from hundreds of data sources; • Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ] RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  5. 5. Hong Kong in DBpedia db:Hong_Kong db:thumbnail subject predicate object 8134 triples RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  6. 6. Hong Kong in DBpedia db:Hong_Kong db:thumbnail Skyscrapers over 350 meters in Hong Kong? select * where { ?s dbpedia-owl:location <http://dbpedia.org/resource/Hong_Kong>. ?s dcterms:subject category:Skyscrapers_over_350_meters. } RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  7. 7. Hong Kong in DBpedia db:Hong_Kong db:thumbnail db:location db:International_Commerce_centre db:thumbnail db:Central_Plaza_(Hong_Kong) dcterms:subject db:thumbnail db:category:Skyscrapers_over_350_meters) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  8. 8. Motivation Traditional Ontological/Semantic Recommender Systems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  9. 9. Motivation Traditional Ontological/Semantic Recommender Systems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. But… • a lot of structured semantic data on the Web; • Implicit feedback are easier to collect; • Top-N Recommendations is a more realistic task. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  10. 10. Motivation Traditional Ontological/Semantic Recommender Systems: • make use of limited domain ontologies; • rely on explicit feedback data; • address the rating prediction task. But… • a lot of structured semantic data on the Web; • Implicit feedback are easier to collect; • Top-N Recommendations is a more realistic task. Challenge: • compute Top-N Item Recommendations from implicit feedback exploiting the Web of Data. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  11. 11. Our approach • Usage of structured semantic data freely available on the Web (Linked Open Data) to describe items DBpedia ontology RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  12. 12. Our approach • Analysis of complex relations between the user preferences and the target item (extraction of path-based features) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  13. 13. Our approach • Analysis of complex relations between the user preferences and the target item (extraction of path-based features) • Formalization of the Top-N Item recommendation problem from implicit feedback in a Learning To Rank setting RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  14. 14. Data model Implicit Feedback Matrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  15. 15. Data model Implicit Feedback Matrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  16. 16. Data model Implicit Feedback Matrix ^ S I1 i2 i3 i4 u1 1 1 0 0 u2 1 0 1 0 u3 0 1 1 0 u4 0 1 0 Knowledge Graph 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  17. 17. Problem formulation  u ^  u ^ I  {i  I | s ui  1} Set of relevant items for u I  {i  I | s ui  0} Set of irrelevant items for u   Iu *  Iu Sample of irrelevant items for u xui  Feature vector D  ^     xui , s ui  i  ( I u  I u * ) TR  u Training Set RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  18. 18. Path-based features path acyclic sequence of relations ( s , .. rl , .. rL ) u3 s i2 p2 e1 p1 i1 xui ( j )   (s, p2 , p1) # pathui ( j ) D  # path d 1 ui (d ) Frequency of pathj in the sub-graph related to u ad i • The more the paths, the more the item is relevant. • Different paths have different meaning. • Not all types of paths are relevant. RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  19. 19. Path-based features i1 xu3i1 ? e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  20. 20. Path-based features path1 (s, s, s) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  21. 21. Path-based features path1 (s, s, s) : 2 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  22. 22. Path-based features path1 (s, s, s) : 2 path2 (s, p2, p1) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  23. 23. Path-based features path1 (s, s, s) : 2 path2 (s, p2, p1) : 2 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  24. 24. Path-based features path1 (s, s, s) : 2 path2 (s, p2, p1) : 2 path3 (s, p2, p3, p1) : 1 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  25. 25. Path-based features path1 (s, s, s) : 2 path2 (s, p2, p1) : 2 path3 (s, p2, p3, p1) : 1 2 xu3i1 (1)  5 2 xu3i1 (2)  5 1 xu3i1 (3)  5 i1 e1 u1 e3 u2 i2 e2 u3 i3 u4 i4 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China e5 e4
  26. 26. Learning the ranking function Point-wise Learning To Rank Learn a prediction function f : D  ^ s.t. f ( xui )  sui Assumption: if f is accurate, then the ranking induced by f should be close to the desired ranking • Simplest LTR technique • Very effective in practice (Yahoo! Learning to Rank Challenge best solution was extremely randomized trees in a standard regression setting) RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  27. 27. BagBoo BagBoo: a scalable hybrid bagging-the-boosting model [D. Pavlov, A. Gorodilov, C. Brunk CIKM2010] • Combination of Random Forest (Bagging) and Gradient Boosted Regression Trees (Boosting) • Combines the high accuracy of gradient boosting with the resistance to overfitting of random forests For b=1 to B: Tb  TR fb  learn GBRT from Tb 1 B f   fb B b 1 RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  28. 28. Evaluation Methodology • Top-N Item recommendation task • Evaluation methodology similar to: [Cremonesi, Koren and Turrin, RecSys 2010] • Evaluation with different user profile size: given 5 given 10 User profile 5 User profile Test Set 10 …… given All User profile Test Set 10 Test Set RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  29. 29. Datasets Subset of Movielens mapped to DBpedia 3,792 users 2,795 movies 104,351 entities Subset of Last.fm mapped to DBpedia 852 users 6,256 artists 150,925 entities Mappings http://sisinflab.poliba.it/mappingdatasets2dbpedia.zip RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  30. 30. Evaluation of different ranking functions Movielens 0,6 0,5 recall@5 0,4 BagBoo 0,3 GBRT Sum 0,2 0,1 0 given 5 given 10 given 20 given 30 given 50 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  31. 31. Evaluation of different ranking functions Last.fm 0,6 0,5 recall@5 0,4 BagBoo 0,3 GBRT Sum 0,2 0,1 0 given 5 given 10 given 20 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  32. 32. Comparative approaches MyMediaLite • BPRMF, Bayesian Personalized Ranking for Matrix Factorization • BPRLin, Linear Model optimized for BPR (Hybrid alg.) • SLIM, Sparse Linear Methods for Top-N Recommender Systems • SMRMF, Soft Margin Ranking Matrix Factorization RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  33. 33. Comparison with other approaches Movielens 0,6 0,5 recall@5 0,4 SPrank BPRMF 0,3 SLIM BPRLin 0,2 SMRMF 0,1 0 given 5 given 10 given 20 given 30 given 50 user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China given All
  34. 34. Comparison with other approaches Last.fm 0,6 0,5 recall@5 0,4 SPrank BPRMF 0,3 SLIM BPRLin 0,2 SMRMF 0,1 0 given 5 given 10 given 20 given All user profile size RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  35. 35. Contributions SPrank: Semantic Path-based ranking  Combination of semantic item descriptions from the Web of Data and implicit feedback  Mining of the semantic graph using path-based features  Learning To Rank setting Future Work:  Deeper analysis of the path-based features  Usage of other Learning To Rank approaches RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
  36. 36. Q&A A Little Semantics Goes a Long Way. Hendler Hypothesis RecSys 2013 – 7th ACM Conference on Recommender Systems October 12-16, 2013 Hong Kong, China
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